# Using tests for special causes in control charts

Use tests for special causes to determine which observations you may need to investigate and to identify specific patterns in your data.

## Which tests for special causes are included in Minitab?

Test 1: One point more than 3σ from center line
Test 1 identifies subgroups that are unusual compared to other subgroups. Test 1 is universally recognized as necessary for detecting out-of-control situations. If small shifts in the process are of interest, you can use Test 2 to supplement Test 1 in order to create a control chart that has greater sensitivity.
Test 2: Nine points in a row on the same side of the center line
Test 2 identifies shifts in the process centering or variation. If small shifts in the process are of interest, you can use Test 2 to supplement Test 1 in order to create a control chart that has greater sensitivity.
Test 3: Six points in a row, all increasing or all decreasing
Test 3 detects trends. This test looks for a long series of consecutive points that consistently increase in value or decrease in value.
Test 4: Fourteen points in a row, alternating up and down
Test 4 detects systematic variation. You want the pattern of variation in a process to be random, but a point that fails Test 4 might indicate that the pattern of variation is predictable.
Test 5: Two out of three points more than 2σ from the center line (same side)
Test 5 detects small shifts in the process.
Test 6: Four out of five points more than 1σ from center line (same side)
Test 6 detects small shifts in the process.
Test 7: Fifteen points in a row within 1σ of center line (either side)
Test 7 detects a pattern of variation that is sometimes mistaken as evidence of good control. This test detects control limits that are too wide. Control limits that are too wide are often caused by stratified data, which occur when a systematic source of variation is present within each subgroup.
Test 8: Eight points in a row more than 1σ from center line (either side)
Test 8 detects a mixture pattern. In a mixture pattern, the points tend to fall away from the center line and instead fall near the control limits.

## Which tests should I use to detect specific patterns of special-cause variation?

Apply certain tests based on your knowledge of the process. If it is likely that your data might contain particular patterns, you can look for them by choosing the appropriate test. Adding more tests makes the chart more sensitive, but may also increase the chance of getting a false signal. When you use several tests together, the chance of obtaining a signal for lack-of-control increases.

### Variables charts

If you are not sure which tests apply in your specific situation, you might try using Tests 1, 2, and 7 when you first establish the control limits based on your data. After the control limits are established, you should use the known values of those limits and Test 7 is no longer needed.
• Test 1 (a point outside the control limits) detects a single out-of-control point.
• Test 2 (9 points in a row on one side of the center line) detects a possible shift in the process.
• Test 7 (too many points in a row within 1 standard deviation of the center line) detects control limits that are too wide. Wide control limits are often caused by stratified data, which occur when you have a systematic source of variation within each subgroup.

### Attributes charts

If you are not sure which tests apply in your specific situation, you might try using Tests 1 and 2 when you first establish the control limits based on your data. After the control limits are established, you should use the known values of those limits.
• Test 1 (a point outside the control limits) detects a single out-of-control point.
• Test 2 (9 points in a row on one side of the center line) detects a possible shift in the process.

## Which tests are available with my control chart?

Tests 1−8 are available for most variables control charts. Note that only tests 1−4 are available for R, S, and moving range charts.

Tests 1−4 are available for the attribute control charts.

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